import pandas as pd
import plotly.express as px
import numpy as np
import matplotlib.pyplot as plt
import os
from mpl_toolkits import mplot3d

from plotly.offline import download_plotlyjs, init_notebook_mode
from plotly.offline import plot, iplot
import plotly.graph_objects as go
%matplotlib inline

#Create a path to where your data is stored.
path = r'C:\Users\jinu5\Desktop\careerfoundry\ML\DataSet'

#Read in the European weather data.
climate = pd.read_csv(os.path.join(path, 'prepared data', 'df_scaled.csv'))
climate

#Reduce to just the mean temperatures
df = climate[['DATE', 'MONTH','BASEL_temp_mean',
 'BELGRADE_temp_mean',
 'BUDAPEST_temp_mean',
 'DEBILT_temp_mean',
 'DUSSELDORF_temp_mean',
 'GDANSK_temp_mean',
 'HEATHROW_temp_mean',
 'KASSEL_temp_mean',
 'LJUBLJANA_temp_mean',
 'MAASTRICHT_temp_mean',
 'MADRID_temp_mean',
 'MUNCHENB_temp_mean',
 'OSLO_temp_mean',
 'ROMA_temp_mean',
 'SONNBLICK_temp_mean',
 'STOCKHOLM_temp_mean',
 'TOURS_temp_mean',
 'VALENTIA_temp_mean']].copy()

df

#You'll need to reduce the dataset to only one year of data. Analyze and pick which year you want to use.

#Drop the DATE and MONTH data as those numbers are not scaled with the rest.
notemp = df.drop(['DATE','MONTH'], axis=1)

#Look at a whisker plot of the data to see variations in temperatures
notemp.boxplot(figsize=(15,15))
plt.xticks(rotation=90)

(array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
        18]),
 [Text(1, 0, 'BASEL_temp_mean'),
  Text(2, 0, 'BELGRADE_temp_mean'),
  Text(3, 0, 'BUDAPEST_temp_mean'),
  Text(4, 0, 'DEBILT_temp_mean'),
  Text(5, 0, 'DUSSELDORF_temp_mean'),
  Text(6, 0, 'GDANSK_temp_mean'),
  Text(7, 0, 'HEATHROW_temp_mean'),
  Text(8, 0, 'KASSEL_temp_mean'),
  Text(9, 0, 'LJUBLJANA_temp_mean'),
  Text(10, 0, 'MAASTRICHT_temp_mean'),
  Text(11, 0, 'MADRID_temp_mean'),
  Text(12, 0, 'MUNCHENB_temp_mean'),
  Text(13, 0, 'OSLO_temp_mean'),
  Text(14, 0, 'ROMA_temp_mean'),
  Text(15, 0, 'SONNBLICK_temp_mean'),
  Text(16, 0, 'STOCKHOLM_temp_mean'),
  Text(17, 0, 'TOURS_temp_mean'),
  Text(18, 0, 'VALENTIA_temp_mean')])

#Reduce your dataset to a single year
dfyear = df[df['DATE'].astype(str).str.contains('2021')] #<-----INSERT YEAR HERE
dfyear.head(20)

dfyear = df[df['DATE'].astype(str).str.startswith('2021')]

dfyear.head(20)

dfyear.describe()

#Pick which weather station you want to use. Below is a 3D visualization of the temperatures for that year.

#Drop the DATE and MONTH data as those numbers are not scaled with the rest.
notempyear = dfyear.drop(['DATE','MONTH'], axis=1)

#Plot ALL weather data for all stations for a year 

#X = weather station
#Y = day of the year
#Z = temperature

#you can click/hold in the graph below to rotate!

fig = go.Figure(data=[go.Surface(z=notempyear.values)])
fig.update_layout(title='Temperatures over time', autosize=False,
                  width=600, height=600)
fig.show()

#We need to make an index for the year. Create a set of data from 1 to 365 (or to 366 if it's a leap year!)
#We'll scale this by 100 as the index is made. This will help teh gradient descent converge 366 = 3.66

i = np.arange(0.01,3.66,0.01) #<---needs to be one GREATER than the total number of days
index = pd.DataFrame(data = i, columns = ['index'])
index

n_rows = dfyear.shape[0]
n_rows

365

#This will translate your chosen weather data into the X and y datasets needed for the optimization function.

X=index.to_numpy().reshape(n_rows,1)
X

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       [3.65]])

ones = np.ones((n_rows,1))
ones.shape

(365, 1)

#Represent x_0 as a vector of 1s for vector computation
ones = np.ones((n_rows,1))
X = np.concatenate((ones, index.to_numpy().reshape(n_rows, 1)), axis=1)
X

array([[1.  , 0.01],
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       [1.  , 3.15],
       [1.  , 3.16],
       [1.  , 3.17],
       [1.  , 3.18],
       [1.  , 3.19],
       [1.  , 3.2 ],
       [1.  , 3.21],
       [1.  , 3.22],
       [1.  , 3.23],
       [1.  , 3.24],
       [1.  , 3.25],
       [1.  , 3.26],
       [1.  , 3.27],
       [1.  , 3.28],
       [1.  , 3.29],
       [1.  , 3.3 ],
       [1.  , 3.31],
       [1.  , 3.32],
       [1.  , 3.33],
       [1.  , 3.34],
       [1.  , 3.35],
       [1.  , 3.36],
       [1.  , 3.37],
       [1.  , 3.38],
       [1.  , 3.39],
       [1.  , 3.4 ],
       [1.  , 3.41],
       [1.  , 3.42],
       [1.  , 3.43],
       [1.  , 3.44],
       [1.  , 3.45],
       [1.  , 3.46],
       [1.  , 3.47],
       [1.  , 3.48],
       [1.  , 3.49],
       [1.  , 3.5 ],
       [1.  , 3.51],
       [1.  , 3.52],
       [1.  , 3.53],
       [1.  , 3.54],
       [1.  , 3.55],
       [1.  , 3.56],
       [1.  , 3.57],
       [1.  , 3.58],
       [1.  , 3.59],
       [1.  , 3.6 ],
       [1.  , 3.61],
       [1.  , 3.62],
       [1.  , 3.63],
       [1.  , 3.64],
       [1.  , 3.65]])

y=dfyear['BASEL_temp_mean'].to_numpy().reshape(n_rows,1) #<----INSERT WEATHER STATION HERE

X.shape, y.shape

((365, 2), (365, 1))

#Look at one year of temperature data over time
plt.scatter(x=index['index'], y=dfyear['BUDAPEST_temp_mean']) #<----INSERT WEATHER STATION HERE
plt.xlabel('X'); plt.ylabel('y');
plt.title('Input dataset');

#what is the min temperature? (Note gradient descent is not actually finding this number)
dfyear['BUDAPEST_temp_mean'].min()

-0.005166671

#what is the max temperature? (Note gradient descent is not actually finding this number)
dfyear['BUDAPEST_temp_mean'].max()

-0.005166671

dfyear

dfyear.describe()

# change the location from Budapest to BASEL
y=dfyear['BASEL_temp_mean'].to_numpy().reshape(n_rows,1) #<----INSERT WEATHER STATION HERE
X.shape, y.shape
#Look at one year of temperature data over time
plt.scatter(x=index['index'], y=dfyear['BASEL_temp_mean']) #<----INSERT WEATHER STATION HERE
plt.xlabel('X'); plt.ylabel('y');
plt.title('Input dataset');

#This computes the loss function for the gradiant descent. DO NOT CHANGE!

def compute_cost(X, y, theta=np.array([[0],[0]])):
    """Given covariate matrix X, the prediction results y and coefficients theta
    compute the loss"""
    
    m = len(y)
    J=0 # initialize loss to zero
    
    # reshape theta
    theta=theta.reshape(2,1)
    
    # calculate the hypothesis - y_hat
    h_x = np.dot(X,theta)
    #print(h_x)
    
    # subtract y from y_hat, square and sum
    error_term = sum((h_x - y)**2)
    
    # divide by twice the number of samples - standard practice.
    loss = error_term/(2*m)
    
    return loss

compute_cost(X,y)

array([0.45003918])

#This is the gradiant descent function. DO NOT CHANGE!

def gradient_descent(X, y, theta=np.array([[0],[0]]),
                    alpha=0.01, num_iterations=1500):
    """
    Solve for theta using Gradient Descent optimiztion technique. 
    Alpha is the learning rate
    """
    m = len(y)
    J_history = []
    theta0_history = []
    theta1_history = []
    theta = theta.reshape(2,1)
    
    for i in range(num_iterations):
        error = (np.dot(X, theta) - y)
        
        term0 = (alpha/m) * sum(error* X[:,0].reshape(m,1))
        term1 = (alpha/m) * sum(error* X[:,1].reshape(m,1))
        
        # update theta
        term_vector = np.array([[term0],[term1]])
        #print(term_vector)
        theta = theta - term_vector.reshape(2,1)
        
        # store history values
        theta0_history.append(theta[0].tolist()[0])
        theta1_history.append(theta[1].tolist()[0])
        J_history.append(compute_cost(X,y,theta).tolist()[0])
        
    return (theta, J_history, theta0_history, theta1_history)

%%time
#This runs your data through a gradiant descent for the starting conditions in 'theta_init.'
#You will need to adjust these numbers

num_iterations=30#<---Decide how many iterations you need. Start small and work up. Over 10,000 iterations will take a few seconds.
theta_init=np.array([[1],[-1]]) #<---this is where you put the guess for [theta0], [theta1]. Start with 1 and 1.
alpha=0.05#<---Decide what your step size is. Try values between 0.1 and 0.00001. You will need to adjust your iterations.
#If your solution is not converging, try a smaller step size.
theta, J_history, theta0_history, theta1_history = gradient_descent(X,y, theta_init,
                                                                   alpha, num_iterations)
theta

CPU times: total: 15.6 ms
Wall time: 41.4 ms

array([[ 0.82303785],
       [-0.30862993]])

#This will plot your loss, theta0, and theta1.If the result looks like a straight line, it's not converging on an answer!
#Your loss (red) should be trending toward 0.

fig, ax1 = plt.subplots()

# plot thetas over time
color='tab:blue'
ax1.plot(theta0_history, label='$\\theta_{0}$', linestyle='--', color=color)
ax1.plot(theta1_history, label='$\\theta_{1}$', linestyle='-', color=color)
# ax1.legend()
ax1.set_xlabel('Iterations'); ax1.set_ylabel('$\\theta$', color=color);
ax1.tick_params(axis='y', labelcolor=color)

# plot loss function over time
color='tab:red'
ax2 = ax1.twinx()
ax2.plot(J_history, label='Loss function', color=color)
ax2.set_title('Values of $\\theta$ and $J(\\theta)$ over iterations')
ax2.set_ylabel('Loss: $J(\\theta)$', color=color)
ax1.tick_params(axis='y', labelcolor=color)

# ax2.legend();
fig.legend();

%%time
# theta range
theta0_vals = np.linspace(-10,10,100) #Look in the chart above for the limits of where theta0 and theta1 appear.
theta1_vals = np.linspace(-10,10,100) #Put those values as the first two "linspace" numbers in these lines
                                      #Select with large margins, maybe +/- 10
J_vals = np.zeros((len(theta0_vals), len(theta1_vals)))

# compute cost for each combination of theta
c1=0; c2=0
for i in theta0_vals:
    for j in theta1_vals:
        t = np.array([i, j])
        J_vals[c1][c2] = compute_cost(X, y, t.transpose()).tolist()[0]
        c2=c2+1
    c1=c1+1
    c2=0 # reinitialize to 0

CPU times: total: 719 ms
Wall time: 2.73 s

#This figure shows the loss function.

#X = Theta0
#Y - Theta1
#Z = Loss
#Find where it is closest to 0 in X and Y!

#you can click/hold in the graph below to rotate!

fig = go.Figure(data=[go.Surface(x=theta1_vals, y=theta0_vals, z=J_vals)])
fig.update_layout(title='Loss function for different thetas', autosize=True,
                  width=600, height=600, xaxis_title='theta0', 
                 yaxis_title='theta1')
fig.show()

#Here is the same figure as above, with the line the loss function takes toward the minimum.

#X = Theta0
#Y - Theta1
#Z = Loss
#black line = path of loss function over the iterations.
#Find where it is closest to 0 in X and Y!

#you can click/hold in the graph below to rotate!

line_marker = dict(color='#101010', width=2)
fig = go.Figure()
fig.add_surface(x=theta1_vals, y=theta0_vals, z=J_vals)
fig.add_scatter3d(x=theta1_history, y=theta0_history, z=J_history, line=line_marker, name='')
#The below line adds a graph of just the loss over iterations in a 2D plane
plt.plot(theta0_history, theta1_history, 'r+');
fig.update_layout(title='Loss function for different thetas', autosize=True,
                  width=600, height=600, xaxis_title='theta0', 
                 yaxis_title='theta1')
fig.show()

#Rerun the optimization above, but this time start closer to the objective!
#Find where the black line ends near the lowest X/Y/Z coordinate and make that your guess below.

num_iterations=30 #<---start with the same iterations as above
theta_init=np.array([[-0.5],[-1.10]]) #<---make a guess as to a more accurate [x],[y] coordinates near the minimum in the graph above.
alpha= 0.01 #<---start with the same step size as above
theta1, J_history1, theta0_history1, theta1_history1 = gradient_descent(X,y, theta_init,
                                                                   alpha, num_iterations)

#Let's look at the new loss path on the function. It should start much closer to the goal

line_marker = dict(color='#101010', width=2)
fig = go.Figure()
fig.add_surface(x=theta1_vals, y=theta0_vals, z=J_vals)
fig.add_scatter3d(x=theta1_history1, y=theta0_history1, z=J_history1, line=line_marker, name='')
#The below line adds a graph of just the loss over iterations in a 2D plane
plt.plot(theta0_history1, theta1_history1, 'r+');
fig.update_layout(title='Loss function for different thetas', autosize=True,
                  width=600, height=600, xaxis_title='theta0', 
                 yaxis_title='theta1')
fig.show()

#This plot shows the convergence similar to above, but only in the X/Y plane (there's no height)

plt.contour(theta0_vals, theta1_vals, J_vals, levels = np.logspace(0,10,1000))
plt.xlabel('$\\theta_{0}$'); plt.ylabel("$\\theta_{1}$")
plt.title("Contour plot of loss function for different values of $\\theta$s");
plt.plot(theta0_history1, theta1_history1, 'r+');

#How well does gradient descent converge? How much do you need to adjust between different weather stations and years?

dfyear = df[df['DATE'].astype(str).str.contains('2010')] #<-----INSERT YEAR HERE
dfyear = df[df['DATE'].astype(str).str.startswith('2010')]
y=dfyear['BASEL_temp_mean'].to_numpy().reshape(n_rows,1) #<----INSERT WEATHER STATION HERE

%%time
#This runs your data through a gradiant descent for the starting conditions in 'theta_init.'
#You will need to adjust these numbers

num_iterations=15 #<---Decide how many iterations you need. Start small and work up. Over 10,000 iterations will take a few seconds.
theta_init=np.array([[-0.5],[1.1]]) #<---this is where you put the guess for [theta0], [theta1]. Start with 1 and 1.
alpha=0.05#<---Decide what your step size is. Try values between 0.1 and 0.00001. You will need to adjust your iterations.
#If your solution is not converging, try a smaller step size.
theta, J_history, theta0_history, theta1_history = gradient_descent(X,y, theta_init,
                                                                   alpha, num_iterations)
theta

CPU times: total: 0 ns
Wall time: 13.3 ms

array([[-0.74479474],
       [ 0.33625032]])

#This will plot your loss, theta0, and theta1.If the result looks like a straight line, it's not converging on an answer!
#Your loss (red) should be trending toward 0.

fig, ax1 = plt.subplots()

# plot thetas over time
color='tab:blue'
ax1.plot(theta0_history, label='$\\theta_{0}$', linestyle='--', color=color)
ax1.plot(theta1_history, label='$\\theta_{1}$', linestyle='-', color=color)
# ax1.legend()
ax1.set_xlabel('Iterations'); ax1.set_ylabel('$\\theta$', color=color);
ax1.tick_params(axis='y', labelcolor=color)

# plot loss function over time
color='tab:red'
ax2 = ax1.twinx()
ax2.plot(J_history, label='Loss function', color=color)
ax2.set_title('Values of $\\theta$ and $J(\\theta)$ over iterations')
ax2.set_ylabel('Loss: $J(\\theta)$', color=color)
ax1.tick_params(axis='y', labelcolor=color)

# ax2.legend();
fig.legend();

line_marker = dict(color='#101010', width=2)
fig = go.Figure()
fig.add_surface(x=theta1_vals, y=theta0_vals, z=J_vals)
fig.add_scatter3d(x=theta1_history, y=theta0_history, z=J_history, line=line_marker, name='')
#The below line adds a graph of just the loss over iterations in a 2D plane
plt.plot(theta0_history, theta1_history, 'r+');
fig.update_layout(title='Loss function for different thetas', autosize=True,
                  width=600, height=600, xaxis_title='theta0', 
                 yaxis_title='theta1')
fig.show()

dfyear = df[df['DATE'].astype(str).str.contains('1998')] #<-----INSERT YEAR HERE
dfyear = df[df['DATE'].astype(str).str.startswith('1998')]
y=dfyear['BASEL_temp_mean'].to_numpy().reshape(n_rows,1) #<----INSERT WEATHER STATION HERE

%%time
#This runs your data through a gradiant descent for the starting conditions in 'theta_init.'
#You will need to adjust these numbers

num_iterations=30 #<---Decide how many iterations you need. Start small and work up. Over 10,000 iterations will take a few seconds.
theta_init=np.array([[-0.1],[0.5]]) #<---this is where you put the guess for [theta0], [theta1]. Start with 1 and 1.
alpha=0.01 #<---Decide what your step size is. Try values between 0.1 and 0.00001. You will need to adjust your iterations.
#If your solution is not converging, try a smaller step size.
theta, J_history, theta0_history, theta1_history = gradient_descent(X,y, theta_init,
                                                                   alpha, num_iterations)
theta

CPU times: total: 15.6 ms
Wall time: 32.3 ms

array([[-0.20958018],
       [ 0.20838722]])

#This will plot your loss, theta0, and theta1.If the result looks like a straight line, it's not converging on an answer!
#Your loss (red) should be trending toward 0.

fig, ax1 = plt.subplots()

# plot thetas over time
color='tab:blue'
ax1.plot(theta0_history, label='$\\theta_{0}$', linestyle='--', color=color)
ax1.plot(theta1_history, label='$\\theta_{1}$', linestyle='-', color=color)
# ax1.legend()
ax1.set_xlabel('Iterations'); ax1.set_ylabel('$\\theta$', color=color);
ax1.tick_params(axis='y', labelcolor=color)

# plot loss function over time
color='tab:red'
ax2 = ax1.twinx()
ax2.plot(J_history, label='Loss function', color=color)
ax2.set_title('Values of $\\theta$ and $J(\\theta)$ over iterations')
ax2.set_ylabel('Loss: $J(\\theta)$', color=color)
ax1.tick_params(axis='y', labelcolor=color)

# ax2.legend();
fig.legend();

line_marker = dict(color='#101010', width=2)
fig = go.Figure()
fig.add_surface(x=theta1_vals, y=theta0_vals, z=J_vals)
fig.add_scatter3d(x=theta1_history, y=theta0_history, z=J_history, line=line_marker, name='')
#The below line adds a graph of just the loss over iterations in a 2D plane
plt.plot(theta0_history, theta1_history, 'r+');
fig.update_layout(title='Loss function for different thetas', autosize=True,
                  width=600, height=600, xaxis_title='theta0', 
                 yaxis_title='theta1')
fig.show()

dfyear = df[df['DATE'].astype(str).str.contains('2019')] #<-----INSERT YEAR HERE
dfyear = df[df['DATE'].astype(str).str.startswith('2019')]
y=dfyear['STOCKHOLM_temp_mean'].to_numpy().reshape(n_rows,1) #<----INSERT WEATHER STATION HERE

%%time
#This runs your data through a gradiant descent for the starting conditions in 'theta_init.'
#You will need to adjust these numbers

num_iterations=30 #<---Decide how many iterations you need. Start small and work up. Over 10,000 iterations will take a few seconds.
theta_init=np.array([[1],[1]]) #<---this is where you put the guess for [theta0], [theta1]. Start with 1 and 1.
alpha=0.05#<---Decide what your step size is. Try values between 0.1 and 0.00001. You will need to adjust your iterations.
#If your solution is not converging, try a smaller step size.
theta, J_history, theta0_history, theta1_history = gradient_descent(X,y, theta_init,
                                                                   alpha, num_iterations)
theta

CPU times: total: 15.6 ms
Wall time: 25.2 ms

array([[ 0.31185703],
       [-0.02013077]])

#This will plot your loss, theta0, and theta1.If the result looks like a straight line, it's not converging on an answer!
#Your loss (red) should be trending toward 0.

fig, ax1 = plt.subplots()

# plot thetas over time
color='tab:blue'
ax1.plot(theta0_history, label='$\\theta_{0}$', linestyle='--', color=color)
ax1.plot(theta1_history, label='$\\theta_{1}$', linestyle='-', color=color)
# ax1.legend()
ax1.set_xlabel('Iterations'); ax1.set_ylabel('$\\theta$', color=color);
ax1.tick_params(axis='y', labelcolor=color)

# plot loss function over time
color='tab:red'
ax2 = ax1.twinx()
ax2.plot(J_history, label='Loss function', color=color)
ax2.set_title('Values of $\\theta$ and $J(\\theta)$ over iterations')
ax2.set_ylabel('Loss: $J(\\theta)$', color=color)
ax1.tick_params(axis='y', labelcolor=color)

# ax2.legend();
fig.legend();

line_marker = dict(color='#101010', width=2)
fig = go.Figure()
fig.add_surface(x=theta1_vals, y=theta0_vals, z=J_vals)
fig.add_scatter3d(x=theta1_history, y=theta0_history, z=J_history, line=line_marker, name='')
#The below line adds a graph of just the loss over iterations in a 2D plane
plt.plot(theta0_history, theta1_history, 'r+');
fig.update_layout(title='Loss function for different thetas', autosize=True,
                  width=600, height=600, xaxis_title='theta0', 
                 yaxis_title='theta1')
fig.show()

dfyear = df[df['DATE'].astype(str).str.contains('2005')] #<-----INSERT YEAR HERE
dfyear = df[df['DATE'].astype(str).str.startswith('2005')]
y=dfyear['STOCKHOLM_temp_mean'].to_numpy().reshape(n_rows,1) #<----INSERT WEATHER STATION HERE

%%time
#This runs your data through a gradiant descent for the starting conditions in 'theta_init.'
#You will need to adjust these numbers

num_iterations=15 #<---Decide how many iterations you need. Start small and work up. Over 10,000 iterations will take a few seconds.
theta_init=np.array([[-2],[0.1]]) #<---this is where you put the guess for [theta0], [theta1]. Start with 1 and 1.
alpha=0.05#<---Decide what your step size is. Try values between 0.1 and 0.00001. You will need to adjust your iterations.
#If your solution is not converging, try a smaller step size.
theta, J_history, theta0_history, theta1_history = gradient_descent(X,y, theta_init,
                                                                   alpha, num_iterations)
theta

CPU times: total: 0 ns
Wall time: 14.8 ms

array([[-1.49680181],
       [ 0.71812687]])

#This will plot your loss, theta0, and theta1.If the result looks like a straight line, it's not converging on an answer!
#Your loss (red) should be trending toward 0.

fig, ax1 = plt.subplots()

# plot thetas over time
color='tab:blue'
ax1.plot(theta0_history, label='$\\theta_{0}$', linestyle='--', color=color)
ax1.plot(theta1_history, label='$\\theta_{1}$', linestyle='-', color=color)
# ax1.legend()
ax1.set_xlabel('Iterations'); ax1.set_ylabel('$\\theta$', color=color);
ax1.tick_params(axis='y', labelcolor=color)

# plot loss function over time
color='tab:red'
ax2 = ax1.twinx()
ax2.plot(J_history, label='Loss function', color=color)
ax2.set_title('Values of $\\theta$ and $J(\\theta)$ over iterations')
ax2.set_ylabel('Loss: $J(\\theta)$', color=color)
ax1.tick_params(axis='y', labelcolor=color)

# ax2.legend();
fig.legend();

line_marker = dict(color='#101010', width=2)
fig = go.Figure()
fig.add_surface(x=theta1_vals, y=theta0_vals, z=J_vals)
fig.add_scatter3d(x=theta1_history, y=theta0_history, z=J_history, line=line_marker, name='')
#The below line adds a graph of just the loss over iterations in a 2D plane
plt.plot(theta0_history, theta1_history, 'r+');
fig.update_layout(title='Loss function for different thetas', autosize=True,
                  width=600, height=600, xaxis_title='theta0', 
                 yaxis_title='theta1')
fig.show()

dfyear = df[df['DATE'].astype(str).str.contains('1995')] #<-----INSERT YEAR HERE
dfyear = df[df['DATE'].astype(str).str.startswith('1995')]
y=dfyear['STOCKHOLM_temp_mean'].to_numpy().reshape(n_rows,1) #<----INSERT WEATHER STATION HERE

%%time
#This runs your data through a gradiant descent for the starting conditions in 'theta_init.'
#You will need to adjust these numbers

num_iterations=15 #<---Decide how many iterations you need. Start small and work up. Over 10,000 iterations will take a few seconds.
theta_init=np.array([[-0.5],[1.1]]) #<---this is where you put the guess for [theta0], [theta1]. Start with 1 and 1.
alpha=0.05#<---Decide what your step size is. Try values between 0.1 and 0.00001. You will need to adjust your iterations.
#If your solution is not converging, try a smaller step size.
theta, J_history, theta0_history, theta1_history = gradient_descent(X,y, theta_init,
                                                                   alpha, num_iterations)
theta

CPU times: total: 0 ns
Wall time: 18.2 ms

array([[-0.72696776],
       [ 0.33896967]])

#This will plot your loss, theta0, and theta1.If the result looks like a straight line, it's not converging on an answer!
#Your loss (red) should be trending toward 0.

fig, ax1 = plt.subplots()

# plot thetas over time
color='tab:blue'
ax1.plot(theta0_history, label='$\\theta_{0}$', linestyle='--', color=color)
ax1.plot(theta1_history, label='$\\theta_{1}$', linestyle='-', color=color)
# ax1.legend()
ax1.set_xlabel('Iterations'); ax1.set_ylabel('$\\theta$', color=color);
ax1.tick_params(axis='y', labelcolor=color)

# plot loss function over time
color='tab:red'
ax2 = ax1.twinx()
ax2.plot(J_history, label='Loss function', color=color)
ax2.set_title('Values of $\\theta$ and $J(\\theta)$ over iterations')
ax2.set_ylabel('Loss: $J(\\theta)$', color=color)
ax1.tick_params(axis='y', labelcolor=color)

# ax2.legend();
fig.legend();

line_marker = dict(color='#101010', width=2)
fig = go.Figure()
fig.add_surface(x=theta1_vals, y=theta0_vals, z=J_vals)
fig.add_scatter3d(x=theta1_history, y=theta0_history, z=J_history, line=line_marker, name='')
#The below line adds a graph of just the loss over iterations in a 2D plane
plt.plot(theta0_history, theta1_history, 'r+');
fig.update_layout(title='Loss function for different thetas', autosize=True,
                  width=600, height=600, xaxis_title='theta0', 
                 yaxis_title='theta1')
fig.show()

dfyear = df[df['DATE'].astype(str).str.contains('2021')] #<-----INSERT YEAR HERE
dfyear = df[df['DATE'].astype(str).str.startswith('2021')]
y=dfyear['VALENTIA_temp_mean'].to_numpy().reshape(n_rows,1) #<----INSERT WEATHER STATION HERE

%%time
#This runs your data through a gradiant descent for the starting conditions in 'theta_init.'
#You will need to adjust these numbers

num_iterations=1000 #<---Decide how many iterations you need. Start small and work up. Over 10,000 iterations will take a few seconds.
theta_init=np.array([[0.5],[0.5]]) #<---this is where you put the guess for [theta0], [theta1]. Start with 1 and 1.
alpha=0.0001#<---Decide what your step size is. Try values between 0.1 and 0.00001. You will need to adjust your iterations.
#If your solution is not converging, try a smaller step size.
theta, J_history, theta0_history, theta1_history = gradient_descent(X,y, theta_init,
                                                                   alpha, num_iterations)
theta

CPU times: total: 312 ms
Wall time: 921 ms

array([[0.3882126 ],
       [0.25471709]])

#This will plot your loss, theta0, and theta1.If the result looks like a straight line, it's not converging on an answer!
#Your loss (red) should be trending toward 0.

fig, ax1 = plt.subplots()

# plot thetas over time
color='tab:blue'
ax1.plot(theta0_history, label='$\\theta_{0}$', linestyle='--', color=color)
ax1.plot(theta1_history, label='$\\theta_{1}$', linestyle='-', color=color)
# ax1.legend()
ax1.set_xlabel('Iterations'); ax1.set_ylabel('$\\theta$', color=color);
ax1.tick_params(axis='y', labelcolor=color)

# plot loss function over time
color='tab:red'
ax2 = ax1.twinx()
ax2.plot(J_history, label='Loss function', color=color)
ax2.set_title('Values of $\\theta$ and $J(\\theta)$ over iterations')
ax2.set_ylabel('Loss: $J(\\theta)$', color=color)
ax1.tick_params(axis='y', labelcolor=color)

# ax2.legend();
fig.legend();

line_marker = dict(color='#101010', width=2)
fig = go.Figure()
fig.add_surface(x=theta1_vals, y=theta0_vals, z=J_vals)
fig.add_scatter3d(x=theta1_history, y=theta0_history, z=J_history, line=line_marker, name='')
#The below line adds a graph of just the loss over iterations in a 2D plane
plt.plot(theta0_history, theta1_history, 'r+');
fig.update_layout(title='Loss function for different thetas', autosize=True,
                  width=600, height=600, xaxis_title='theta0', 
                 yaxis_title='theta1')
fig.show()

dfyear = df[df['DATE'].astype(str).str.contains('2007')] #<-----INSERT YEAR HERE
dfyear = df[df['DATE'].astype(str).str.startswith('2007')]
y=dfyear['VALENTIA_temp_mean'].to_numpy().reshape(n_rows,1) #<----INSERT WEATHER STATION HERE

%%time
#This runs your data through a gradiant descent for the starting conditions in 'theta_init.'
#You will need to adjust these numbers

num_iterations=5000 #<---Decide how many iterations you need. Start small and work up. Over 10,000 iterations will take a few seconds.
theta_init=np.array([[0.5],[-1]]) #<---this is where you put the guess for [theta0], [theta1]. Start with 1 and 1.
alpha=0.001#<---Decide what your step size is. Try values between 0.1 and 0.00001. You will need to adjust your iterations.
#If your solution is not converging, try a smaller step size.
theta, J_history, theta0_history, theta1_history = gradient_descent(X,y, theta_init,
                                                                   alpha, num_iterations)
theta

CPU times: total: 1 s
Wall time: 3.76 s

array([[0.10816721],
       [0.1654668 ]])

#This will plot your loss, theta0, and theta1.If the result looks like a straight line, it's not converging on an answer!
#Your loss (red) should be trending toward 0.

fig, ax1 = plt.subplots()

# plot thetas over time
color='tab:blue'
ax1.plot(theta0_history, label='$\\theta_{0}$', linestyle='--', color=color)
ax1.plot(theta1_history, label='$\\theta_{1}$', linestyle='-', color=color)
# ax1.legend()
ax1.set_xlabel('Iterations'); ax1.set_ylabel('$\\theta$', color=color);
ax1.tick_params(axis='y', labelcolor=color)

# plot loss function over time
color='tab:red'
ax2 = ax1.twinx()
ax2.plot(J_history, label='Loss function', color=color)
ax2.set_title('Values of $\\theta$ and $J(\\theta)$ over iterations')
ax2.set_ylabel('Loss: $J(\\theta)$', color=color)
ax1.tick_params(axis='y', labelcolor=color)

# ax2.legend();
fig.legend();

line_marker = dict(color='#101010', width=2)
fig = go.Figure()
fig.add_surface(x=theta1_vals, y=theta0_vals, z=J_vals)
fig.add_scatter3d(x=theta1_history, y=theta0_history, z=J_history, line=line_marker, name='')
#The below line adds a graph of just the loss over iterations in a 2D plane
plt.plot(theta0_history, theta1_history, 'r+');
fig.update_layout(title='Loss function for different thetas', autosize=True,
                  width=600, height=600, xaxis_title='theta0', 
                 yaxis_title='theta1')
fig.show()

dfyear = df[df['DATE'].astype(str).str.contains('1998')] #<-----INSERT YEAR HERE
dfyear = df[df['DATE'].astype(str).str.startswith('1998')]
y=dfyear['VALENTIA_temp_mean'].to_numpy().reshape(n_rows,1) #<----INSERT WEATHER STATION HERE

%%time
#This runs your data through a gradiant descent for the starting conditions in 'theta_init.'
#You will need to adjust these numbers

num_iterations=500 #<---Decide how many iterations you need. Start small and work up. Over 10,000 iterations will take a few seconds.
theta_init=np.array([[-0.1],[0.3]]) #<---this is where you put the guess for [theta0], [theta1]. Start with 1 and 1.
alpha=0.001#<---Decide what your step size is. Try values between 0.1 and 0.00001. You will need to adjust your iterations.
#If your solution is not converging, try a smaller step size.
theta, J_history, theta0_history, theta1_history = gradient_descent(X,y, theta_init,
                                                                   alpha, num_iterations)
theta

CPU times: total: 344 ms
Wall time: 552 ms

array([[-0.15834828],
       [ 0.22357141]])

#This will plot your loss, theta0, and theta1.If the result looks like a straight line, it's not converging on an answer!
#Your loss (red) should be trending toward 0.

fig, ax1 = plt.subplots()

# plot thetas over time
color='tab:blue'
ax1.plot(theta0_history, label='$\\theta_{0}$', linestyle='--', color=color)
ax1.plot(theta1_history, label='$\\theta_{1}$', linestyle='-', color=color)
# ax1.legend()
ax1.set_xlabel('Iterations'); ax1.set_ylabel('$\\theta$', color=color);
ax1.tick_params(axis='y', labelcolor=color)

# plot loss function over time
color='tab:red'
ax2 = ax1.twinx()
ax2.plot(J_history, label='Loss function', color=color)
ax2.set_title('Values of $\\theta$ and $J(\\theta)$ over iterations')
ax2.set_ylabel('Loss: $J(\\theta)$', color=color)
ax1.tick_params(axis='y', labelcolor=color)

# ax2.legend();
fig.legend();

line_marker = dict(color='#101010', width=2)
fig = go.Figure()
fig.add_surface(x=theta1_vals, y=theta0_vals, z=J_vals)
fig.add_scatter3d(x=theta1_history, y=theta0_history, z=J_history, line=line_marker, name='')
#The below line adds a graph of just the loss over iterations in a 2D plane
plt.plot(theta0_history, theta1_history, 'r+');
fig.update_layout(title='Loss function for different thetas', autosize=True,
                  width=600, height=600, xaxis_title='theta0', 
                 yaxis_title='theta1')
fig.show()

	DATE	MONTH	BASEL_cloud_cover	BASEL_wind_speed	BASEL_humidity	BASEL_pressure	BASEL_global_radiation	BASEL_precipitation	BASEL_snow_depth	BASEL_sunshine	...	VALENTIA_cloud_cover	VALENTIA_humidity	VALENTIA_pressure	VALENTIA_global_radiation	VALENTIA_precipitation	VALENTIA_snow_depth	VALENTIA_sunshine	VALENTIA_temp_mean	VALENTIA_temp_min	VALENTIA_temp_max
0	19600101	1	0.660514	-0.02793	0.826097	-0.001949	-1.101066	-0.265148	-0.179228	-0.902918	...	-0.443701	0.761754	-1.299744	-0.806427	-0.088407	-0.024706	0.372147	-0.668215	-0.519743	-0.752237
1	19600102	1	0.244897	-0.02793	0.735760	-0.001949	-1.058108	1.658760	-0.179228	-0.810126	...	0.783085	1.183580	-1.262455	-1.042055	0.503361	-0.024706	-0.829285	-0.548046	-0.629054	-0.407141
2	19600103	1	1.076130	-0.02793	1.277781	-0.001949	-1.251420	0.155707	-0.179228	-1.065304	...	0.783085	1.183580	-0.432779	-1.136306	-0.396127	-0.024706	-1.009500	-0.067372	0.054135	-0.177078
3	19600104	1	-1.001953	-0.02793	1.458455	-0.001949	-0.821838	-0.445514	-0.179228	-0.114186	...	0.783085	0.480538	0.387574	-1.183432	0.669056	-0.024706	-1.039536	-0.998679	-0.164486	-0.838511
4	19600105	1	0.244897	-0.02793	1.729466	-0.001949	-0.746661	-0.164944	-0.179228	0.187388	...	-1.670486	-0.363113	1.729970	-0.794645	-0.490810	-0.024706	0.672505	-1.509396	-1.339569	-1.471186
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
22945	20221027	10	-1.833187	-0.02793	0.284075	1.037367	-0.005632	-0.004619	-0.179228	0.720943	...	-0.443701	-0.081896	-0.003958	-0.005290	-0.005560	-0.024706	-0.018319	-0.007287	-0.000520	-0.004530
22946	20221028	10	0.244897	-0.02793	0.103401	0.976231	-0.005632	-0.004619	-0.179228	0.187388	...	-0.443701	-0.081896	-0.003958	-0.005290	-0.005560	-0.024706	-0.018319	-0.007287	-0.000520	-0.004530
22947	20221029	10	-0.586336	-0.02793	0.013064	0.716402	-0.005632	-0.004619	-0.179228	0.349774	...	-0.443701	-0.081896	-0.003958	-0.005290	-0.005560	-0.024706	-0.018319	-0.007287	-0.000520	-0.004530
22948	20221030	10	-0.170720	-0.02793	0.374412	0.487141	-0.005632	-0.004619	-0.179228	0.280180	...	-0.443701	-0.081896	-0.003958	-0.005290	-0.005560	-0.024706	-0.018319	-0.007287	-0.000520	-0.004530
22949	20221031	10	-0.170720	-0.02793	0.735760	0.196744	-0.005632	-0.004619	-0.179228	-0.322968	...	-0.443701	-0.081896	-0.003958	-0.005290	-0.005560	-0.024706	-0.018319	-0.007287	-0.000520	-0.004530

	DATE	MONTH	BASEL_temp_mean	BELGRADE_temp_mean	BUDAPEST_temp_mean	DEBILT_temp_mean	DUSSELDORF_temp_mean	GDANSK_temp_mean	HEATHROW_temp_mean	KASSEL_temp_mean	LJUBLJANA_temp_mean	MAASTRICHT_temp_mean	MADRID_temp_mean	MUNCHENB_temp_mean	OSLO_temp_mean	ROMA_temp_mean	SONNBLICK_temp_mean	STOCKHOLM_temp_mean	TOURS_temp_mean	VALENTIA_temp_mean
0	19600101	1	-0.528623	-1.016876	-1.099163	-0.114356	-0.105836	-0.927601	-0.106469	-0.182904	-1.370824	-0.097084	-0.988280	-0.265742	-0.186575	-1.280450	-0.124331	-0.391072	-0.257321	-0.668215
1	19600102	1	-0.582946	-1.107669	-1.110927	-0.367511	-0.370915	-0.825294	-0.892676	-0.212437	-1.043881	-0.232112	-0.691740	-0.353714	-0.368598	-0.539569	-0.650834	-0.415953	-0.335759	-0.548046
2	19600103	1	-0.257010	-1.084971	-1.063873	-0.509912	-0.532908	-0.940389	-0.490837	-0.389635	-0.741156	-0.487164	-0.853490	-0.403983	-0.550620	-0.876333	-0.650834	-0.615003	-0.210258	-0.067372
3	19600104	1	-0.555784	-1.209812	-1.146217	-0.525734	-0.577088	-1.042696	-0.316124	-0.493001	-0.910682	-0.472161	-0.624345	-0.642763	-0.417137	-0.775304	-0.943336	-0.764290	-0.069069	-0.998679
4	19600105	1	-1.003946	-1.209812	-1.087400	-0.320045	-0.444548	-0.978754	-0.403481	-0.552067	-0.862246	-0.307127	-0.381721	-0.906678	-0.332193	-0.926848	-0.621584	-0.503037	-0.037694	-1.509396
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
22945	20221027	10	0.747959	0.628750	-0.005167	0.898267	1.042841	0.440753	0.906865	-0.005705	0.481853	1.268193	0.683127	0.664244	0.395898	-0.000746	0.826301	0.517090	1.295755	-0.007287
22946	20221028	10	0.856604	0.367720	-0.005167	0.945734	1.278467	0.466330	0.802037	-0.005705	0.263891	1.313202	0.561815	0.890456	0.541516	-0.000746	1.074927	0.641496	1.358505	-0.007287
22947	20221029	10	0.856604	0.083991	-0.005167	0.914089	1.101748	0.786039	0.924336	-0.005705	0.300218	1.208181	0.548336	1.053832	0.395898	-0.000746	1.221178	0.716139	1.405568	-0.007287
22948	20221030	10	0.680055	0.265577	-0.005167	0.692578	0.880848	0.376811	0.697210	-0.005705	0.397090	0.923123	0.103526	0.689378	-0.065226	-0.000746	1.235803	0.019467	0.668249	-0.007287
22949	20221031	10	0.422023	0.197483	-0.005167	0.439422	0.659949	0.159409	0.435140	-0.005705	0.348654	0.773092	-0.112140	0.488300	0.335224	-0.000746	0.987177	0.293160	0.809438	-0.007287

	DATE	MONTH	BASEL_temp_mean	BELGRADE_temp_mean	BUDAPEST_temp_mean	DEBILT_temp_mean	DUSSELDORF_temp_mean	GDANSK_temp_mean	HEATHROW_temp_mean	KASSEL_temp_mean	LJUBLJANA_temp_mean	MAASTRICHT_temp_mean	MADRID_temp_mean	MUNCHENB_temp_mean	OSLO_temp_mean	ROMA_temp_mean	SONNBLICK_temp_mean	STOCKHOLM_temp_mean	TOURS_temp_mean	VALENTIA_temp_mean
771	19620210	2	-1.139752	-1.289256	-1.169744	-0.937112	-0.974707	-0.914813	-1.137274	-0.965530	-1.068099	-1.057280	-1.338737	-1.145458	-0.477811	-0.707951	-0.723959	-0.453275	-1.261329	-0.247625
772	19620211	2	-1.139752	-1.380049	-1.216797	-0.921290	-0.989434	-0.659045	-1.049918	-0.847397	-1.201298	-0.892246	-1.217425	-1.120324	-0.635564	-0.556407	-0.811710	-0.615003	-0.665199	-0.728299
773	19620212	2	-0.528623	-0.892036	-1.122690	-0.399156	-0.488728	-0.710199	-0.735435	-0.670199	-0.934900	-0.517170	-0.893927	-0.177771	-0.866127	-0.876333	-0.738584	-0.851374	-1.010327	-0.548046
774	19620213	2	-1.085430	-0.369975	-0.628627	-1.174445	-1.372326	-1.119426	-1.014975	-1.187028	-0.862246	-1.282325	-0.826531	-0.956948	-0.951071	-1.213097	-1.104212	-1.436081	-0.602449	-1.148890
775	19620214	2	-1.465688	-1.391398	-1.099163	-1.316845	-1.401779	-1.196156	-1.539113	-1.438059	-1.201298	-1.492368	-0.988280	-1.497345	-1.120958	-1.431994	-2.332720	-1.510724	-1.449581	-1.569480
776	19620215	2	-1.587914	-1.607032	-1.416775	-1.269379	-1.475413	-1.439135	-1.294515	-1.526658	-1.322388	-1.447359	-1.325258	-1.748692	-1.557813	-2.021331	-2.873848	-1.908823	-1.465269	-1.269058
777	19620216	2	-0.691591	-1.709174	-1.628516	-0.430800	-0.518182	-1.081061	-0.980032	-0.611133	-1.164971	-0.532173	-1.298299	-0.705600	-0.356463	-2.156037	-1.747716	-0.913576	-0.806388	-0.457920
778	19620217	2	-0.990365	-1.153066	-0.922712	-0.889645	-1.048340	-1.081061	-0.875205	-0.847397	-0.801701	-1.057280	-0.947843	-1.032352	-0.671969	-1.011039	-1.513715	-1.560487	-0.727950	-0.337751
779	19620218	2	-1.166914	-1.311954	-1.146217	-0.842178	-0.915801	-1.157791	-0.822791	-0.906464	-0.813810	-1.072283	-0.893927	-1.208295	-1.084554	-1.314127	-2.157219	-1.610249	-1.073078	-0.698257
780	19620219	2	-1.180494	-1.334652	-1.099163	-0.636489	-0.797988	-0.914813	-0.805320	-0.581600	-1.043881	-0.757219	-0.705220	-0.894111	-0.587025	-1.583538	-1.148087	-1.100185	-1.041702	-0.698257
4423	19720210	2	-0.868139	-0.517514	-0.816841	-1.237734	-0.871621	-0.876447	-1.294515	-0.640666	-0.922791	-0.832234	-0.759136	-0.881544	-0.781183	-0.926848	-0.475333	-0.975779	-1.229954	-1.659606
4424	19720211	2	-0.610107	-0.472117	-0.910949	-0.842178	-0.665448	-0.902024	-1.154745	-0.611133	-1.055990	-0.667200	-0.705220	-0.743302	-0.829722	-1.280450	-0.840960	-0.913576	-0.571074	-1.629564
4425	19720212	2	-0.678010	-0.415371	-0.581573	-0.842178	-0.753808	-0.722987	-1.014975	-0.640666	-0.995445	-0.757219	-0.866969	-0.831274	-0.975340	-1.078392	-1.177337	-0.876255	-0.790700	-1.509396
4426	19720213	2	-0.827397	-0.653703	-0.558047	-1.047867	-0.989434	-0.978754	-1.049918	-0.891697	-0.741156	-0.817231	-0.610866	-0.994650	-0.635564	-1.128906	-1.308963	-0.739408	-0.947577	-1.509396
4427	19720214	2	-0.922462	-0.937432	-0.769788	-0.968756	-0.930527	-0.876447	-1.014975	-0.699732	-0.813810	-0.937255	-1.150029	-0.919246	-0.732643	-0.859495	-1.367464	-0.739408	-0.822076	-1.509396
4428	19720215	2	-0.936043	-1.164415	-0.805078	-1.221912	-0.694901	-0.838082	-1.242102	-0.729265	-0.632175	-0.922252	-1.163509	-1.007217	-0.902531	-1.078392	-1.674591	-0.714527	-1.104453	-1.299100
4429	19720216	2	-0.732333	-0.892036	-0.875659	-0.763067	-0.709628	-0.889236	-0.735435	-0.655433	-1.225516	-0.697206	-1.150029	-1.195728	-0.902531	-1.499347	-1.133462	-0.751849	-0.712262	-1.239016
4430	19720217	2	-0.922462	-0.823941	-0.910949	-0.668134	-0.503455	-0.876447	-1.014975	-0.714499	-0.789592	-0.637194	-1.446569	-1.070054	-0.914666	-1.280450	-0.548458	-0.751849	-0.853451	-1.389227
4431	19720218	2	-0.854559	-0.767195	-0.875659	-1.206090	-1.077794	-0.850871	-1.119803	-0.965530	-0.777483	-0.967261	-1.271341	-1.082622	-1.157363	-0.455378	-0.446083	-0.888695	-0.759325	-1.509396
4432	19720219	2	-1.275559	-0.937432	-0.816841	-1.285201	-1.166153	-1.017119	-1.259573	-0.758798	-0.753265	-1.237316	-1.203946	-0.592494	-1.363655	-0.489054	-0.548458	-0.913576	-1.041702	-2.170323

	DATE	MONTH	BASEL_temp_mean	BELGRADE_temp_mean	BUDAPEST_temp_mean	DEBILT_temp_mean	DUSSELDORF_temp_mean	GDANSK_temp_mean	HEATHROW_temp_mean	KASSEL_temp_mean	LJUBLJANA_temp_mean	MAASTRICHT_temp_mean	MADRID_temp_mean	MUNCHENB_temp_mean	OSLO_temp_mean	ROMA_temp_mean	SONNBLICK_temp_mean	STOCKHOLM_temp_mean	TOURS_temp_mean	VALENTIA_temp_mean
22281	20210101	1	-1.112591	-0.653703	-0.005167	-1.111156	-1.239786	-1.272886	-1.713826	-0.005705	-0.995445	-1.222313	-1.473528	-1.107756	-0.744778	-0.000746	-1.206588	-0.540359	-1.496644	-0.007287
22282	20210102	1	-1.221236	-0.551561	-0.005167	-1.016223	-1.195607	-1.081061	-1.504171	-0.005705	-0.741156	-1.282325	-1.716151	-1.271132	-0.671969	-0.000746	-0.884835	-0.702087	-1.763333	-0.007287
22283	20210103	1	-1.384204	-0.415371	-0.005167	-1.063690	-1.136700	-1.042696	-1.381872	-0.005705	-0.862246	-1.207310	-1.608319	-1.208295	-0.951071	-0.000746	-1.016461	-0.938458	-1.465269	-0.007287
22284	20210104	1	-1.329882	-0.517514	-0.005167	-1.142801	-1.269240	-0.940389	-1.259573	-0.005705	-0.886464	-1.297328	-1.648756	-1.245997	-1.145228	-0.000746	-1.191962	-0.863814	-1.575082	-0.007287
22285	20210105	1	-1.384204	-0.699100	-0.005167	-1.126979	-1.239786	-0.799717	-1.399343	-0.005705	-0.874355	-1.267322	-1.810505	-1.296267	-1.582083	-0.000746	-1.382089	-0.975779	-1.496644	-0.007287
22286	20210106	1	-1.384204	-0.619656	-0.005167	-1.206090	-1.180880	-0.902024	-1.189688	-0.005705	-1.152862	-1.222313	-1.823984	-1.283700	-1.873319	-0.000746	-1.382089	-1.125066	-1.402518	-0.007287
22287	20210107	1	-1.234817	-0.755846	-0.005167	-1.174445	-1.328146	-0.991543	-1.783711	-0.005705	-1.164971	-1.312331	-1.958775	-1.132891	-1.424330	-0.000746	-1.703841	-0.913576	-1.386830	-0.007287
22288	20210108	1	-1.397785	-1.073622	-0.005167	-1.411779	-1.431233	-0.965966	-1.906010	-0.005705	-1.164971	-1.387347	-2.066608	-1.145458	-1.339386	-0.000746	-1.981718	-0.838933	-1.826084	-0.007287
22289	20210109	1	-1.682979	-1.402747	-0.005167	-1.680757	-1.490139	-1.004331	-1.766240	-0.005705	-1.407151	-1.537377	-2.093566	-1.283700	-1.387925	-0.000746	-1.776966	-0.975779	-1.888835	-0.007287
22290	20210110	1	-1.601495	-1.425445	-0.005167	-1.332668	-1.460686	-0.953178	-1.608998	-0.005705	-1.395042	-1.447359	-1.850942	-1.736125	-1.218037	-0.000746	-1.747716	-0.975779	-1.575082	-0.007287
22291	20210111	1	-1.764463	-1.470842	-0.005167	-0.794712	-1.210333	-1.004331	-1.207159	-0.005705	-1.491914	-1.207310	-2.093566	-1.899501	-1.460734	-0.000746	-1.850092	-0.975779	-1.841772	-0.007287
22292	20210112	1	-1.126172	-1.504889	-0.005167	-0.763067	-0.842167	-1.004331	-0.665550	-0.005705	-1.770421	-0.832234	-2.470980	-1.145458	-1.751971	-0.000746	-1.177337	-1.025542	-0.884826	-0.007287
22293	20210113	1	-0.786655	-1.357351	-0.005167	-0.905467	-1.180880	-1.170580	-1.049918	-0.005705	-1.576677	-1.147298	-2.255315	-1.057487	-1.254442	-0.000746	-1.557590	-1.025542	-0.335759	-0.007287
22294	20210114	1	-1.139752	-1.414096	-0.005167	-1.395957	-1.387053	-1.119426	-1.049918	-0.005705	-1.395042	-1.342338	-2.026170	-1.145458	-1.521409	-0.000746	-1.689216	-1.411200	-0.367134	-0.007287
22295	20210115	1	-1.533592	-1.391398	-0.005167	-1.395957	-1.490139	-1.413558	-1.608998	-0.005705	-1.552459	-1.552380	-1.958775	-1.547614	-1.982533	-0.000746	-1.908592	-1.871501	-0.994640	-0.007287
22296	20210116	1	-1.859527	-1.731872	-0.005167	-1.459245	-1.534319	-1.503077	-1.294515	-0.005705	-1.782530	-1.552380	-1.716151	-1.484778	-2.006803	-0.000746	-2.157219	-1.286794	-1.731958	-0.007287
22297	20210117	1	-1.316301	-1.845364	-0.005167	-1.047867	-1.401779	-2.986526	-1.032446	-0.005705	-1.758312	-1.102289	-1.797026	-1.447075	-1.618487	-0.000746	-1.747716	-1.149947	-0.775013	-0.007287
22298	20210118	1	-1.180494	-1.788618	-0.005167	-0.826356	-0.871621	-2.641240	-0.892676	-0.005705	-1.431369	-0.862240	-1.540923	-1.019785	-1.145228	-0.000746	-1.718466	-1.087745	-1.277017	-0.007287
22299	20210119	1	-1.343462	-1.164415	-0.005167	-0.367511	-0.665448	-2.027399	-0.648078	-0.005705	-1.237625	-0.697206	-1.554402	-0.906678	-0.853992	-0.000746	-1.162712	-0.888695	-0.994640	-0.007287
22300	20210120	1	-1.003946	-0.619656	-0.005167	-0.161822	-0.341462	-0.953178	-0.246239	-0.005705	-0.692720	-0.172100	-1.513965	-0.579927	-0.696239	-0.000746	-0.665459	-0.801611	-0.382822	-0.007287

	DATE	MONTH	BASEL_temp_mean	BELGRADE_temp_mean	BUDAPEST_temp_mean	DEBILT_temp_mean	DUSSELDORF_temp_mean	GDANSK_temp_mean	HEATHROW_temp_mean	KASSEL_temp_mean	LJUBLJANA_temp_mean	MAASTRICHT_temp_mean	MADRID_temp_mean	MUNCHENB_temp_mean	OSLO_temp_mean	ROMA_temp_mean	SONNBLICK_temp_mean	STOCKHOLM_temp_mean	TOURS_temp_mean	VALENTIA_temp_mean
count	3.650000e+02	365.000000	365.000000	365.000000	3.650000e+02	365.000000	365.000000	365.000000	365.000000	3.650000e+02	365.000000	365.000000	365.000000	365.000000	365.000000	3.650000e+02	365.000000	365.000000	365.000000	3.650000e+02
mean	2.021067e+07	6.526027	0.032909	0.128766	-5.166671e-03	0.072520	0.013712	0.088705	0.085045	-5.705221e-03	0.101863	0.062647	0.089456	0.090138	0.102767	-7.461640e-04	0.045401	0.099155	0.116518	-7.287352e-03
std	3.454755e+02	3.452584	0.949455	1.005278	1.042263e-17	0.966594	0.975583	1.066883	1.010767	3.039933e-17	1.014904	0.964474	1.009745	0.926124	1.061531	4.777038e-18	1.026706	1.048349	0.985446	4.603327e-17
min	2.021010e+07	1.000000	-2.212624	-2.049648	-5.166671e-03	-2.503513	-2.668269	-2.986526	-2.185550	-5.705221e-03	-1.855184	-2.572588	-2.470980	-2.125714	-2.152421	-7.461640e-04	-2.815348	-2.294481	-2.281025	-7.287352e-03
25%	2.021040e+07	4.000000	-0.745913	-0.631005	-5.166671e-03	-0.683956	-0.680175	-0.748564	-0.683021	-5.705221e-03	-0.704829	-0.667200	-0.664782	-0.667898	-0.574890	-7.461640e-04	-0.636209	-0.577681	-0.649512	-7.287352e-03
50%	2.021070e+07	7.000000	-0.012558	-0.040850	-5.166671e-03	0.043867	-0.061656	-0.006839	-0.001641	-5.705221e-03	-0.087270	-0.022069	-0.085181	0.035875	0.068257	-7.461640e-04	0.036546	0.019467	0.056432	-7.287352e-03
75%	2.021100e+07	10.000000	0.883765	0.935177	-5.166671e-03	0.993200	0.880848	1.029017	0.994221	-5.705221e-03	0.941995	0.923123	0.817918	0.865322	1.063315	-7.461640e-04	0.855551	0.940070	0.966314	-7.287352e-03
max	2.021123e+07	12.000000	2.078863	2.263027	-5.166671e-03	2.211512	2.559684	2.320641	2.391923	-5.705221e-03	2.092351	2.423428	2.489325	2.109492	2.082643	-7.461640e-04	2.215685	2.358296	2.111511	-7.287352e-03

????????????????????????????????¶

(UN)fortunately, the data of year & location I chose - 2021, Budapest is not normal. I checked the data in 2021 again, and found that some location like Kassel, Roma, etc. have same issue.¶

We don't know exactly now, but we record this because it may affect machine learning.¶

Investigate at least three different weather stations and three different years and find values where the gradient descent converges.¶

Bael 2010¶

BASEL 1998¶

STOCLHOLM 1: 2019¶

STOCKHOLM 2: 2005¶

STOCKHOLM 3: 1995¶

VALENTIA 1: 2021¶

VALENTIA 2: 2007¶

VALENTIA 3: 1998¶

	index
0	0.01
1	0.02
2	0.03
3	0.04
4	0.05
...	...
360	3.61
361	3.62
362	3.63
363	3.64
364	3.65